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          数据仓库分层
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        <h3 id="一、为什么要分层"><a href="#一、为什么要分层" class="headerlink" title="一、为什么要分层"></a>一、为什么要分层</h3><p>（1）<strong>把复杂问题简单化</strong>：将复杂的任务分解为多层来完成，每一层只处理简单的任务，方便定位问题</p>
<p>（2）<strong>减少重复开发</strong>：规范数据分层，通过<strong>中间层数据</strong>，能够减少极大的重复计算，增加一次计算结果的复用性</p>
<span id="more"></span>

<p>（3）<strong>隔离原始数据</strong>：不论是数据的异常还是数据的敏感性，使真实数据与统计数据<strong>解耦</strong>开</p>
<p>（4）<strong>清晰数据结构：</strong>每一个数据分层都有它的作用域，这样我们在使用表的时候能更方便地定位和理解</p>
<p>（5）<strong>方便数据血缘追踪：</strong>简单来说，我们最终给业务呈现的是一个能直接使用业务表，但是它的来源有很多，如果有一张来源表出问题了，我们希望能够快速准确地定位到问题，并清楚它的危害范围。</p>
<h3 id="二、数仓分层思想"><a href="#二、数仓分层思想" class="headerlink" title="二、数仓分层思想"></a>二、数仓分层思想</h3><p>数据分层，每个企业根据自己的业务需求可以分成不同的层次，但是最基础的分层思想，理论上数据分为三个层：<strong>数据运营层、数据仓库层、数据服务层</strong>。基于这个基础分层之上，再提交信息的层次，来满足不同的业务需求。</p>
<p><img src="/myblog/2021/09/30/%E6%95%B0%E6%8D%AE%E4%BB%93%E5%BA%93%EF%BC%9A%E5%88%86%E5%B1%82/1.png" alt="image-20210831141811506"></p>
<p><strong>数据运营层（ODS）</strong></p>
<p>Operate data store（操作数据-存储），是最接近数据源中数据的一层，数据源中的数据，经过抽取、洗净、传输，也就说传说中的ETL之后，装入ODS层。本层的数据，总体上大多是按照源头业务系统的分类方式而分类的。例如：MySQL里面的一张表可以通过sqoop之间抽取到ODS层<br>ODS层数据的来源方式：</p>
<ul>
<li>业务库<br>经常会使用sqoop来抽取，比如我们每天定时抽取一次。在实时方面， 可以考虑用canal监听mysql的binlog，实时接入即可。</li>
<li>埋点日志<br>线上系统会打入各种日志，这些日志一般以文件的形式保存，我们可以选择用flume定时抽取，也可以用用spark streaming或者Flink来实时接入，当然，kafka也会是一个关键的角色。</li>
<li>消息队列<br>来自ActiveMQ、Kafka的数据等</li>
</ul>
<p><strong>数据仓库层（DW）</strong></p>
<p><code>Data warehouse（数据仓库）</code>。在这里，<code>从ODS层中获得的数据按照主题建立各种数据模型</code>。例如<code>以研究人的旅游消费为主题的数据集中</code>，便可以结合航空公司的登机出行信息，以及银联系统的刷卡记录，进行结合分析，产生数据集。在这里，我们需要了解四个概念：维（dimension）、事实（Fact）、指标（Index）和粒度（ Granularity）。</p>
<figure class="highlight text"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">DW数据分层，由下到上为 DWD,DWB,DWS</span><br><span class="line">	DWD：data warehouse detail   细节数据层，是业务层与数据仓库的隔离层。</span><br><span class="line">	DWB：data warehouse base     基础数据层，存储的是客观数据，一般用作中间层，可以认为是大量指标的数据层。</span><br><span class="line">	DWS：data warehouse service  服务数据层，基于DWB上的基础数据，整合汇总成分析某一个主题域的服务数据，一般是宽表</span><br></pre></td></tr></table></figure>

<p><strong>数据服务层/应用层（ADS）：</strong></p>
<p>Application Data Service(应用数据服务)。该层主要是提供数据产品和数据分析使用的数据，一般会存放在ES、MySQL等系统中供线上系统使用，也可能会存在Hive或者Druid中供数据分析和数据挖掘使用。例如：我们经常说的报表数据，或者说那种大宽表，一般就放在这里。</p>

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